Analysis of FOXO1 mutations in diffuse large B-cell lymphoma
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) accounts for 30% to 40% of newly diagnosed lymphomas and has an overall cure rate of approximately 60%. Previously, we observed FOXO1 mutations in non-Hodgkin lymphoma patient samples. To explore the effects of FOXO1 mutations, we assessed FOXO1 status in 279 DLBCL patient samples and 22 DLBCL-derived cell lines. FOXO1 mutations were found in 8.6% (24/279) of DLBCL cases: 92.3% (24/26) of mutations were in the first exon, 46.2% (12/26) were recurrent mutations affecting the N-terminal region, and another 38.5% (10/26) affected the Forkhead DNA binding domain. Recurrent mutations in the N-terminal region resulted in diminished T24 phosphorylation, loss of interaction with 14-3-3, and nuclear retention. FOXO1 mutation was associated with decreased overall survival in patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (P = .037), independent of cell of origin (COO) and the revised International Prognostic Index (R-IPI). This association was particularly evident (P = .003) in patients in the low-risk R-IPI categories. The independent relationship of mutations in FOXO1 to survival, transcending the prognostic influence of the R-IPI and COO, indicates that FOXO1 mutation is a novel prognostic factor that plays an important role in DLBCL pathogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".